Sparse modeling using orthogonal forward regression with PRESS statistic and regularization

The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights regression models based on an approach of directly optimizing model generalization capability. This is achieved by utilizing the delete-1 cross validation concept and the associated leave-one-out test error also known as the predicted residual sums of squares (PRESS) statistic, without resorting to any other validation data set for model evaluation in the model construction process. Computational efficiency is ensured using an orthogonal forward regression, but the algorithm incrementally minimizes the PRESS statistic instead of the usual sum of the squared training errors. A local regularization method can naturally be incorporated into the model selection procedure to further enforce model sparsity. The proposed algorithm is fully automatic, and the user is not required to specify any criterion to terminate the model construction procedure. Comparisons with some of the existing state-of-art modeling methods are given, and several examples are included to demonstrate the ability of the proposed algorithm to effectively construct sparse models that generalize well.

A robust nonlinear identification algorithm using PRESS statistic and forward regression

This letter introduces a new robust nonlinear identification algorithm using the Predicted REsidual Sums of Squares (PRESS) statistic and for-ward regression. The major contribution is to compute the PRESS statistic within a framework of a forward orthogonalization process and hence construct a model with a good generalization property. Based on the properties of the PRESS statistic the proposed algorithm can achieve a fully automated procedure without resort to any other validation data set for iterative model evaluation.

Automatic nonlinear predictive model-construction algorithm using forward regression and the PRESS statistic

An automatic nonlinear predictive model-construction algorithm is introduced based on forward regression and the predicted-residual-sums-of-squares (PRESS) statistic. The proposed algorithm is based on the fundamental concept of evaluating a model's generalisation capability through crossvalidation. This is achieved by using the PRESS statistic as a cost function to optimise model structure. In particular, the proposed algorithm is developed with the aim of achieving computational efficiency, such that the computational effort, which would usually be extensive in the computation of the PRESS statistic, is reduced or minimised. The computation of PRESS is simplified by avoiding a matrix inversion through the use of the orthogonalisation procedure inherent in forward regression, and is further reduced significantly by the introduction of a forward-recursive formula. Based on the properties of the PRESS statistic, the proposed algorithm can achieve a fully automated procedure without resort to any other validation data set for iterative model evaluation. Numerical examples are used to demonstrate the efficacy of the algorithm.

Orthogonal space-time block coding over time-selective fading channels: a PIC detector for the Gi systems

All the orthogonal space-time block coding (O-STBC) schemes are based on the following assumption: the channel remains static over the entire length of the codeword. However, time selective fading channels do exist, and in many cases the conventional O-STBC detectors can suffer from a large error floor in the high signal-to-noise ratio (SNR) cases. This paper addresses such an issue by introducing a parallel interference cancellation (PIC) based detector for the Gi coded systems (i=3 and 4).

A robust non-orthogonal space-time block code for four transmit antennas

Several non-orthogonal space-time block coding (NO-STBC) schemes have recently been proposed to achieve full rate transmission. Some of these schemes, however, suffer from weak robustness: their channel matrices will become ill conditioned in the case of highly correlated channels (HCC). To address this issue, this paper derives a family of robust NO-STBC schemes for four Tx antennas based on the worst case of HCC. These codes turned out to be a superset of Jafarkhani's quasi-orthogonal STBC codes. A computationally affordable linear decoder is also proposed. Although these codes achieve a similar performance to the non-robust schemes under normal channel conditions, they offer a strong robustness against HCC (although possibly yielding a poorer performance). Finally, computer simulations are presented to verify the algorithm design.

Built environment research in West Africa current trends and future directions

The countries in West Africa (WA) are pushing for socio-economic development. The construction sector has an important part to play in helping to realise these aspirations. This necessitates an increased emphasis on research in the built environment, as a key contributor to developing capacity, knowledge and technologies for the sector. The West Africa Built Environment Research (WABER) conference was initiated in 2008. The objective was to: help young built environment researchers in West Africa (WA) to develop their research work and skills through constructive face-to-face interaction with their peers and experienced international academics; supply a platform for interaction among more senior academics and an outlet for disseminating their research work; and to serve as a vehicle for developing the built environment field in Africa. Three conferences have so far been organised, 2009 - 2011, bringing together ~300 academics, researchers and practitioners from the WA region. This paper draws on content analysis of the 189 papers in the proceedings of three conferences: 2009 (25); 2010 (57) and 2011 (107). These papers provide a window into current research priorities and trends and, thus, offer an opportunity to understand the kinds of research work undertaken by built environment researchers in West Africa. The aim is to illuminate the main research themes and methods that are currently pursued and the limitations thereof. The findings lay bare some of the many challenges that are faced by academics in WA and provide suggestions for alternative directions for future research and development work with indications of a potential research agenda.